Join List of Lists in Python

Pick your poison: Various methods explained

itertools: Efficiency in one line

itertools.chain.from_iterable is designed for tasks like this. It creates one iterator, saving memory and increasing speed:

from itertools import chain
# Lightning fast like a cheetah on a sugar rush 🐆
flattened_list = list(chain.from_iterable(list_of_lists))


This method avoids the burden of multiple iterators and doesn't make any intermediate lists, making it your best bet for performance.

Sum: A shortcut, with a small catch

sum() with an empty initial list is another solution:

# It's like magic, but with a twist 🎩🐰
flattened_list = sum(list_of_lists, [])


Keep in mind, with large lists, performance can drop. This is due to continuous creation of intermediate lists, known as 'Schlemiel the Painter's algorithm'.

Custom function: The Swiss knife

A recursive flatten function works for both lists and tuples. It manages lists of variable depths and types:

# It's like that handy friend who always has duct tape 🦾
def flatten(container):
    for i in container:
        if isinstance(i, (list, tuple)):
            yield from flatten(i)
        else:
            yield i

flattened_list = list(flatten(list_of_lists))


This function brilliantly demonstrates the power of recursive calls to handle multiple depths of nested lists or tuples.

When, why, what: Making the right choice

The big data puzzle

itertools.chain() is efficient, but for large data, consider memory usage. Use generator expressions:

# Handling big data like a champ 💪
flattened_list = (item for sublist in list_of_lists for item in sublist)


This syntax keeps memory usage low by not creating a list in memory.

The map and extend combo

Combine the extend() feature with map() to avoid explicit loops:

# The power couple of Python programming 💑
flattened_list = sum(map(lambda lst: lst[:], list_of_lists), [])


Though it might seem complex at first glance, this combo integrates list slicing with sum.

Compatibility with nestlings

Custom flatten functions can handle not just lists, but tuples and other iterables as well. As a fairytale ending, you'll get back exactly what you put in, type-wise.

Python pro tips

Readability is king

Shortcuts might be tempting, but remember the Zen of Python: Readability counts. Keep the code simple.

Purposeful nesting

Don't flatten lists arbitrarily. Sometimes, the nested structure carries meaning, like representing matrix data.

Try-except blocks

They come in handy when you're unsure if the input is uniformly nested or if you might encounter non-iterable elements.